Overview

Brought to you by YData

Dataset statistics

Number of variables25
Number of observations1200
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory234.5 KiB
Average record size in memory200.1 B

Variable types

Categorical17
Numeric8

Alerts

Medical_History_Anxiety is highly overall correlated with Medical_History_Depression and 1 other fieldsHigh correlation
Medical_History_Depression is highly overall correlated with Medical_History_AnxietyHigh correlation
Medical_History_PTSD is highly overall correlated with Medical_History_AnxietyHigh correlation
Panic_Attack_Frequency has 130 (10.8%) zeros Zeros
Duration_Minutes has 28 (2.3%) zeros Zeros
Heart_Rate has 17 (1.4%) zeros Zeros
Caffeine_Intake has 199 (16.6%) zeros Zeros
Exercise_Frequency has 198 (16.5%) zeros Zeros
Alcohol_Consumption has 127 (10.6%) zeros Zeros
Panic_Score has 105 (8.8%) zeros Zeros

Reproduction

Analysis started2025-01-28 06:28:45.795324
Analysis finished2025-01-28 06:28:52.265992
Duration6.47 seconds
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

Medical_History_Anxiety
Categorical

High correlation 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size9.5 KiB
1.0
614 
0.0
586 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3600
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 614
51.2%
0.0 586
48.8%

Length

2025-01-28T11:58:52.327673image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-28T11:58:52.387673image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 614
51.2%
0.0 586
48.8%

Most occurring characters

ValueCountFrequency (%)
0 1786
49.6%
. 1200
33.3%
1 614
 
17.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2400
66.7%
Other Punctuation 1200
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1786
74.4%
1 614
 
25.6%
Other Punctuation
ValueCountFrequency (%)
. 1200
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3600
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1786
49.6%
. 1200
33.3%
1 614
 
17.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3600
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1786
49.6%
. 1200
33.3%
1 614
 
17.1%

Medical_History_Depression
Categorical

High correlation 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size9.5 KiB
0.0
851 
1.0
349 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3600
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 851
70.9%
1.0 349
29.1%

Length

2025-01-28T11:58:52.478621image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-28T11:58:52.644609image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 851
70.9%
1.0 349
29.1%

Most occurring characters

ValueCountFrequency (%)
0 2051
57.0%
. 1200
33.3%
1 349
 
9.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2400
66.7%
Other Punctuation 1200
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2051
85.5%
1 349
 
14.5%
Other Punctuation
ValueCountFrequency (%)
. 1200
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3600
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2051
57.0%
. 1200
33.3%
1 349
 
9.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3600
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2051
57.0%
. 1200
33.3%
1 349
 
9.7%

Medical_History_PTSD
Categorical

High correlation 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size9.5 KiB
0.0
963 
1.0
237 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3600
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 963
80.2%
1.0 237
 
19.8%

Length

2025-01-28T11:58:52.737288image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-28T11:58:52.792288image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 963
80.2%
1.0 237
 
19.8%

Most occurring characters

ValueCountFrequency (%)
0 2163
60.1%
. 1200
33.3%
1 237
 
6.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2400
66.7%
Other Punctuation 1200
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2163
90.1%
1 237
 
9.9%
Other Punctuation
ValueCountFrequency (%)
. 1200
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3600
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2163
60.1%
. 1200
33.3%
1 237
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3600
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2163
60.1%
. 1200
33.3%
1 237
 
6.6%

Trigger_Caffeine
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size9.5 KiB
0.0
998 
1.0
202 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3600
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 998
83.2%
1.0 202
 
16.8%

Length

2025-01-28T11:58:52.860868image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-28T11:58:52.921751image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 998
83.2%
1.0 202
 
16.8%

Most occurring characters

ValueCountFrequency (%)
0 2198
61.1%
. 1200
33.3%
1 202
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2400
66.7%
Other Punctuation 1200
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2198
91.6%
1 202
 
8.4%
Other Punctuation
ValueCountFrequency (%)
. 1200
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3600
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2198
61.1%
. 1200
33.3%
1 202
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3600
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2198
61.1%
. 1200
33.3%
1 202
 
5.6%

Trigger_PTSD
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size9.5 KiB
0.0
995 
1.0
205 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3600
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 995
82.9%
1.0 205
 
17.1%

Length

2025-01-28T11:58:52.981107image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-28T11:58:53.042952image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 995
82.9%
1.0 205
 
17.1%

Most occurring characters

ValueCountFrequency (%)
0 2195
61.0%
. 1200
33.3%
1 205
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2400
66.7%
Other Punctuation 1200
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2195
91.5%
1 205
 
8.5%
Other Punctuation
ValueCountFrequency (%)
. 1200
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3600
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2195
61.0%
. 1200
33.3%
1 205
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3600
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2195
61.0%
. 1200
33.3%
1 205
 
5.7%

Trigger_Phobia
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size9.5 KiB
0.0
997 
1.0
203 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3600
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 997
83.1%
1.0 203
 
16.9%

Length

2025-01-28T11:58:53.104045image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-28T11:58:53.158778image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 997
83.1%
1.0 203
 
16.9%

Most occurring characters

ValueCountFrequency (%)
0 2197
61.0%
. 1200
33.3%
1 203
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2400
66.7%
Other Punctuation 1200
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2197
91.5%
1 203
 
8.5%
Other Punctuation
ValueCountFrequency (%)
. 1200
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3600
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2197
61.0%
. 1200
33.3%
1 203
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3600
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2197
61.0%
. 1200
33.3%
1 203
 
5.6%
Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size9.5 KiB
0.0
1003 
1.0
197 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3600
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1003
83.6%
1.0 197
 
16.4%

Length

2025-01-28T11:58:53.219601image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-28T11:58:53.277149image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1003
83.6%
1.0 197
 
16.4%

Most occurring characters

ValueCountFrequency (%)
0 2203
61.2%
. 1200
33.3%
1 197
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2400
66.7%
Other Punctuation 1200
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2203
91.8%
1 197
 
8.2%
Other Punctuation
ValueCountFrequency (%)
. 1200
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3600
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2203
61.2%
. 1200
33.3%
1 197
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3600
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2203
61.2%
. 1200
33.3%
1 197
 
5.5%

Trigger_Stress
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size9.5 KiB
0.0
1013 
1.0
187 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3600
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1013
84.4%
1.0 187
 
15.6%

Length

2025-01-28T11:58:53.362211image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-28T11:58:53.428241image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1013
84.4%
1.0 187
 
15.6%

Most occurring characters

ValueCountFrequency (%)
0 2213
61.5%
. 1200
33.3%
1 187
 
5.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2400
66.7%
Other Punctuation 1200
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2213
92.2%
1 187
 
7.8%
Other Punctuation
ValueCountFrequency (%)
. 1200
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3600
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2213
61.5%
. 1200
33.3%
1 187
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3600
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2213
61.5%
. 1200
33.3%
1 187
 
5.2%

Trigger_Unknown
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size9.5 KiB
0.0
994 
1.0
206 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3600
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 994
82.8%
1.0 206
 
17.2%

Length

2025-01-28T11:58:53.485239image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-28T11:58:53.540838image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 994
82.8%
1.0 206
 
17.2%

Most occurring characters

ValueCountFrequency (%)
0 2194
60.9%
. 1200
33.3%
1 206
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2400
66.7%
Other Punctuation 1200
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2194
91.4%
1 206
 
8.6%
Other Punctuation
ValueCountFrequency (%)
. 1200
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3600
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2194
60.9%
. 1200
33.3%
1 206
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3600
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2194
60.9%
. 1200
33.3%
1 206
 
5.7%

Sweating
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size9.5 KiB
1.0
836 
0.0
364 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3600
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row0.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 836
69.7%
0.0 364
30.3%

Length

2025-01-28T11:58:53.600509image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-28T11:58:53.655026image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 836
69.7%
0.0 364
30.3%

Most occurring characters

ValueCountFrequency (%)
0 1564
43.4%
. 1200
33.3%
1 836
23.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2400
66.7%
Other Punctuation 1200
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1564
65.2%
1 836
34.8%
Other Punctuation
ValueCountFrequency (%)
. 1200
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3600
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1564
43.4%
. 1200
33.3%
1 836
23.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3600
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1564
43.4%
. 1200
33.3%
1 836
23.2%
Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size9.5 KiB
1.0
746 
0.0
454 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3600
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 746
62.2%
0.0 454
37.8%

Length

2025-01-28T11:58:53.762555image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-28T11:58:53.877116image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 746
62.2%
0.0 454
37.8%

Most occurring characters

ValueCountFrequency (%)
0 1654
45.9%
. 1200
33.3%
1 746
20.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2400
66.7%
Other Punctuation 1200
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1654
68.9%
1 746
31.1%
Other Punctuation
ValueCountFrequency (%)
. 1200
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3600
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1654
45.9%
. 1200
33.3%
1 746
20.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3600
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1654
45.9%
. 1200
33.3%
1 746
20.7%

Dizziness
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size9.5 KiB
1.0
620 
0.0
580 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3600
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.0 620
51.7%
0.0 580
48.3%

Length

2025-01-28T11:58:53.951870image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-28T11:58:54.004042image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 620
51.7%
0.0 580
48.3%

Most occurring characters

ValueCountFrequency (%)
0 1780
49.4%
. 1200
33.3%
1 620
 
17.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2400
66.7%
Other Punctuation 1200
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1780
74.2%
1 620
 
25.8%
Other Punctuation
ValueCountFrequency (%)
. 1200
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3600
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1780
49.4%
. 1200
33.3%
1 620
 
17.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3600
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1780
49.4%
. 1200
33.3%
1 620
 
17.2%

Chest_Pain
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size9.5 KiB
0.0
713 
1.0
487 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3600
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 713
59.4%
1.0 487
40.6%

Length

2025-01-28T11:58:54.060421image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-28T11:58:54.113547image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 713
59.4%
1.0 487
40.6%

Most occurring characters

ValueCountFrequency (%)
0 1913
53.1%
. 1200
33.3%
1 487
 
13.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2400
66.7%
Other Punctuation 1200
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1913
79.7%
1 487
 
20.3%
Other Punctuation
ValueCountFrequency (%)
. 1200
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3600
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1913
53.1%
. 1200
33.3%
1 487
 
13.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3600
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1913
53.1%
. 1200
33.3%
1 487
 
13.5%

Trembling
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size9.5 KiB
0.0
610 
1.0
590 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3600
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 610
50.8%
1.0 590
49.2%

Length

2025-01-28T11:58:54.172996image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-28T11:58:54.227371image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 610
50.8%
1.0 590
49.2%

Most occurring characters

ValueCountFrequency (%)
0 1810
50.3%
. 1200
33.3%
1 590
 
16.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2400
66.7%
Other Punctuation 1200
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1810
75.4%
1 590
 
24.6%
Other Punctuation
ValueCountFrequency (%)
. 1200
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3600
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1810
50.3%
. 1200
33.3%
1 590
 
16.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3600
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1810
50.3%
. 1200
33.3%
1 590
 
16.4%

Medication
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size9.5 KiB
0.0
700 
1.0
500 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3600
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 700
58.3%
1.0 500
41.7%

Length

2025-01-28T11:58:54.287373image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-28T11:58:54.349219image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 700
58.3%
1.0 500
41.7%

Most occurring characters

ValueCountFrequency (%)
0 1900
52.8%
. 1200
33.3%
1 500
 
13.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2400
66.7%
Other Punctuation 1200
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1900
79.2%
1 500
 
20.8%
Other Punctuation
ValueCountFrequency (%)
. 1200
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3600
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1900
52.8%
. 1200
33.3%
1 500
 
13.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3600
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1900
52.8%
. 1200
33.3%
1 500
 
13.9%

Smoking
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size9.5 KiB
0.0
875 
1.0
325 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3600
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 875
72.9%
1.0 325
 
27.1%

Length

2025-01-28T11:58:54.410351image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-28T11:58:54.463101image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 875
72.9%
1.0 325
 
27.1%

Most occurring characters

ValueCountFrequency (%)
0 2075
57.6%
. 1200
33.3%
1 325
 
9.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2400
66.7%
Other Punctuation 1200
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2075
86.5%
1 325
 
13.5%
Other Punctuation
ValueCountFrequency (%)
. 1200
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3600
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2075
57.6%
. 1200
33.3%
1 325
 
9.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3600
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2075
57.6%
. 1200
33.3%
1 325
 
9.0%

Therapy
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size9.5 KiB
1.0
605 
0.0
595 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3600
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.0 605
50.4%
0.0 595
49.6%

Length

2025-01-28T11:58:54.526960image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-28T11:58:54.582600image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 605
50.4%
0.0 595
49.6%

Most occurring characters

ValueCountFrequency (%)
0 1795
49.9%
. 1200
33.3%
1 605
 
16.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2400
66.7%
Other Punctuation 1200
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1795
74.8%
1 605
 
25.2%
Other Punctuation
ValueCountFrequency (%)
. 1200
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3600
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1795
49.9%
. 1200
33.3%
1 605
 
16.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3600
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1795
49.9%
. 1200
33.3%
1 605
 
16.8%

Panic_Attack_Frequency
Real number (ℝ)

Zeros 

Distinct10
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.49027778
Minimum0
Maximum1
Zeros130
Zeros (%)10.8%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2025-01-28T11:58:54.640424image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.22222222
median0.44444444
Q30.77777778
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.55555556

Descriptive statistics

Standard deviation0.31640538
Coefficient of variation (CV)0.64535941
Kurtosis-1.2176754
Mean0.49027778
Median Absolute Deviation (MAD)0.22222222
Skewness0.013703994
Sum588.33333
Variance0.10011236
MonotonicityNot monotonic
2025-01-28T11:58:54.703548image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0.2222222222 130
10.8%
0 130
10.8%
0.7777777778 128
10.7%
0.8888888889 127
10.6%
0.5555555556 124
10.3%
0.3333333333 122
10.2%
0.4444444444 122
10.2%
0.1111111111 110
9.2%
0.6666666667 108
9.0%
1 99
8.2%
ValueCountFrequency (%)
0 130
10.8%
0.1111111111 110
9.2%
0.2222222222 130
10.8%
0.3333333333 122
10.2%
0.4444444444 122
10.2%
0.5555555556 124
10.3%
0.6666666667 108
9.0%
0.7777777778 128
10.7%
0.8888888889 127
10.6%
1 99
8.2%
ValueCountFrequency (%)
1 99
8.2%
0.8888888889 127
10.6%
0.7777777778 128
10.7%
0.6666666667 108
9.0%
0.5555555556 124
10.3%
0.4444444444 122
10.2%
0.3333333333 122
10.2%
0.2222222222 130
10.8%
0.1111111111 110
9.2%
0 130
10.8%

Duration_Minutes
Real number (ℝ)

Zeros 

Distinct40
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.49724359
Minimum0
Maximum1
Zeros28
Zeros (%)2.3%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2025-01-28T11:58:54.787195image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.051282051
Q10.25641026
median0.48717949
Q30.74358974
95-th percentile0.97435897
Maximum1
Range1
Interquartile range (IQR)0.48717949

Descriptive statistics

Standard deviation0.2923059
Coefficient of variation (CV)0.58785253
Kurtosis-1.1391063
Mean0.49724359
Median Absolute Deviation (MAD)0.25641026
Skewness0.039993859
Sum596.69231
Variance0.085442741
MonotonicityNot monotonic
2025-01-28T11:58:54.872069image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
0.4102564103 40
 
3.3%
0.7948717949 39
 
3.2%
1 39
 
3.2%
0.358974359 38
 
3.2%
0.5384615385 37
 
3.1%
0.3076923077 37
 
3.1%
0.6923076923 34
 
2.8%
0.8717948718 33
 
2.8%
0.9487179487 33
 
2.8%
0.4871794872 33
 
2.8%
Other values (30) 837
69.8%
ValueCountFrequency (%)
0 28
2.3%
0.02564102564 28
2.3%
0.05128205128 32
2.7%
0.07692307692 29
2.4%
0.1025641026 30
2.5%
0.1282051282 29
2.4%
0.1538461538 21
1.8%
0.1794871795 32
2.7%
0.2051282051 29
2.4%
0.2307692308 32
2.7%
ValueCountFrequency (%)
1 39
3.2%
0.9743589744 27
2.2%
0.9487179487 33
2.8%
0.9230769231 23
1.9%
0.8974358974 21
1.8%
0.8717948718 33
2.8%
0.8461538462 22
1.8%
0.8205128205 22
1.8%
0.7948717949 39
3.2%
0.7692307692 25
2.1%

Heart_Rate
Real number (ℝ)

Zeros 

Distinct80
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.51015823
Minimum0
Maximum1
Zeros17
Zeros (%)1.4%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2025-01-28T11:58:55.002365image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.037974684
Q10.25316456
median0.51898734
Q30.7721519
95-th percentile0.96202532
Maximum1
Range1
Interquartile range (IQR)0.51898734

Descriptive statistics

Standard deviation0.29582167
Coefficient of variation (CV)0.57986259
Kurtosis-1.2119262
Mean0.51015823
Median Absolute Deviation (MAD)0.26582278
Skewness-0.035392565
Sum612.18987
Variance0.087510461
MonotonicityNot monotonic
2025-01-28T11:58:55.120402image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3164556962 24
 
2.0%
0.8860759494 22
 
1.8%
0.2025316456 22
 
1.8%
0.5189873418 22
 
1.8%
0.7215189873 21
 
1.8%
0.6075949367 21
 
1.8%
1 20
 
1.7%
0.1139240506 20
 
1.7%
0.01265822785 20
 
1.7%
0.8607594937 19
 
1.6%
Other values (70) 989
82.4%
ValueCountFrequency (%)
0 17
1.4%
0.01265822785 20
1.7%
0.0253164557 16
1.3%
0.03797468354 13
1.1%
0.05063291139 11
0.9%
0.06329113924 11
0.9%
0.07594936709 15
1.2%
0.08860759494 7
 
0.6%
0.1012658228 13
1.1%
0.1139240506 20
1.7%
ValueCountFrequency (%)
1 20
1.7%
0.9873417722 18
1.5%
0.9746835443 15
1.2%
0.9620253165 18
1.5%
0.9493670886 17
1.4%
0.9367088608 14
1.2%
0.9240506329 17
1.4%
0.9113924051 11
0.9%
0.8987341772 15
1.2%
0.8860759494 22
1.8%

Caffeine_Intake
Real number (ℝ)

Zeros 

Distinct6
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.50783333
Minimum0
Maximum1
Zeros199
Zeros (%)16.6%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2025-01-28T11:58:55.187504image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.2
median0.6
Q30.8
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.6

Descriptive statistics

Standard deviation0.34337088
Coefficient of variation (CV)0.67614875
Kurtosis-1.275038
Mean0.50783333
Median Absolute Deviation (MAD)0.2
Skewness-0.034929421
Sum609.4
Variance0.11790356
MonotonicityNot monotonic
2025-01-28T11:58:55.243663image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 209
17.4%
0.8 204
17.0%
0.6 200
16.7%
0 199
16.6%
0.4 198
16.5%
0.2 190
15.8%
ValueCountFrequency (%)
0 199
16.6%
0.2 190
15.8%
0.4 198
16.5%
0.6 200
16.7%
0.8 204
17.0%
1 209
17.4%
ValueCountFrequency (%)
1 209
17.4%
0.8 204
17.0%
0.6 200
16.7%
0.4 198
16.5%
0.2 190
15.8%
0 199
16.6%

Exercise_Frequency
Real number (ℝ)

Zeros 

Distinct7
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4925
Minimum0
Maximum1
Zeros198
Zeros (%)16.5%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2025-01-28T11:58:55.300663image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.16666667
median0.5
Q30.83333333
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.66666667

Descriptive statistics

Standard deviation0.34358607
Coefficient of variation (CV)0.69763668
Kurtosis-1.3125954
Mean0.4925
Median Absolute Deviation (MAD)0.33333333
Skewness0.023839904
Sum591
Variance0.11805139
MonotonicityNot monotonic
2025-01-28T11:58:55.360181image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 198
16.5%
1 182
15.2%
0.1666666667 170
14.2%
0.8333333333 170
14.2%
0.3333333333 162
13.5%
0.5 162
13.5%
0.6666666667 156
13.0%
ValueCountFrequency (%)
0 198
16.5%
0.1666666667 170
14.2%
0.3333333333 162
13.5%
0.5 162
13.5%
0.6666666667 156
13.0%
0.8333333333 170
14.2%
1 182
15.2%
ValueCountFrequency (%)
1 182
15.2%
0.8333333333 170
14.2%
0.6666666667 156
13.0%
0.5 162
13.5%
0.3333333333 162
13.5%
0.1666666667 170
14.2%
0 198
16.5%

Sleep_Hours
Real number (ℝ)

Distinct51
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.49631667
Minimum0
Maximum1
Zeros10
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2025-01-28T11:58:55.430709image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.06
Q10.26
median0.5
Q30.72
95-th percentile0.96
Maximum1
Range1
Interquartile range (IQR)0.46

Descriptive statistics

Standard deviation0.28112505
Coefficient of variation (CV)0.56642274
Kurtosis-1.1395614
Mean0.49631667
Median Absolute Deviation (MAD)0.24
Skewness0.050981698
Sum595.58
Variance0.079031292
MonotonicityNot monotonic
2025-01-28T11:58:55.524136image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.16 38
 
3.2%
0.7 33
 
2.8%
0.62 31
 
2.6%
0.4 31
 
2.6%
0.32 31
 
2.6%
0.6 30
 
2.5%
0.98 29
 
2.4%
0.38 28
 
2.3%
0.96 28
 
2.3%
0.58 28
 
2.3%
Other values (41) 893
74.4%
ValueCountFrequency (%)
0 10
 
0.8%
0.02 18
1.5%
0.04 27
2.2%
0.06 18
1.5%
0.08 19
1.6%
0.1 19
1.6%
0.12 20
1.7%
0.14 24
2.0%
0.16 38
3.2%
0.18 24
2.0%
ValueCountFrequency (%)
1 4
 
0.3%
0.98 29
2.4%
0.96 28
2.3%
0.94 23
1.9%
0.92 19
1.6%
0.9 24
2.0%
0.88 25
2.1%
0.86 20
1.7%
0.84 22
1.8%
0.82 21
1.8%

Alcohol_Consumption
Real number (ℝ)

Zeros 

Distinct10
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.49074074
Minimum0
Maximum1
Zeros127
Zeros (%)10.6%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2025-01-28T11:58:55.596854image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.22222222
median0.44444444
Q30.77777778
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.55555556

Descriptive statistics

Standard deviation0.32251091
Coefficient of variation (CV)0.65719205
Kurtosis-1.2362013
Mean0.49074074
Median Absolute Deviation (MAD)0.27777778
Skewness0.043116357
Sum588.88889
Variance0.10401329
MonotonicityNot monotonic
2025-01-28T11:58:55.669081image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0.1111111111 132
11.0%
0.6666666667 129
10.8%
0 127
10.6%
1 125
10.4%
0.2222222222 121
10.1%
0.5555555556 120
10.0%
0.3333333333 117
9.8%
0.4444444444 113
9.4%
0.8888888889 112
9.3%
0.7777777778 104
8.7%
ValueCountFrequency (%)
0 127
10.6%
0.1111111111 132
11.0%
0.2222222222 121
10.1%
0.3333333333 117
9.8%
0.4444444444 113
9.4%
0.5555555556 120
10.0%
0.6666666667 129
10.8%
0.7777777778 104
8.7%
0.8888888889 112
9.3%
1 125
10.4%
ValueCountFrequency (%)
1 125
10.4%
0.8888888889 112
9.3%
0.7777777778 104
8.7%
0.6666666667 129
10.8%
0.5555555556 120
10.0%
0.4444444444 113
9.4%
0.3333333333 117
9.8%
0.2222222222 121
10.1%
0.1111111111 132
11.0%
0 127
10.6%

Panic_Score
Real number (ℝ)

Zeros 

Distinct10
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.50768519
Minimum0
Maximum1
Zeros105
Zeros (%)8.8%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2025-01-28T11:58:55.761654image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.22222222
median0.55555556
Q30.77777778
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.55555556

Descriptive statistics

Standard deviation0.31035061
Coefficient of variation (CV)0.61130522
Kurtosis-1.1532939
Mean0.50768519
Median Absolute Deviation (MAD)0.22222222
Skewness-0.021647301
Sum609.22222
Variance0.096317499
MonotonicityNot monotonic
2025-01-28T11:58:55.843333image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0.3333333333 139
11.6%
0.4444444444 130
10.8%
0.5555555556 128
10.7%
0.6666666667 123
10.2%
0.8888888889 123
10.2%
0.7777777778 120
10.0%
1 113
9.4%
0.1111111111 111
9.2%
0.2222222222 108
9.0%
0 105
8.8%
ValueCountFrequency (%)
0 105
8.8%
0.1111111111 111
9.2%
0.2222222222 108
9.0%
0.3333333333 139
11.6%
0.4444444444 130
10.8%
0.5555555556 128
10.7%
0.6666666667 123
10.2%
0.7777777778 120
10.0%
0.8888888889 123
10.2%
1 113
9.4%
ValueCountFrequency (%)
1 113
9.4%
0.8888888889 123
10.2%
0.7777777778 120
10.0%
0.6666666667 123
10.2%
0.5555555556 128
10.7%
0.4444444444 130
10.8%
0.3333333333 139
11.6%
0.2222222222 108
9.0%
0.1111111111 111
9.2%
0 105
8.8%

Interactions

2025-01-28T11:58:51.297753image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-28T11:58:47.195768image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-28T11:58:47.828440image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-28T11:58:48.382676image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-28T11:58:48.931211image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-28T11:58:49.486510image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-28T11:58:50.171019image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-28T11:58:50.756998image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-28T11:58:51.458080image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-28T11:58:47.295702image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-28T11:58:47.931159image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-28T11:58:48.437551image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-28T11:58:48.998999image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-28T11:58:49.545487image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-28T11:58:50.286166image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-28T11:58:50.815942image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-28T11:58:51.548521image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-28T11:58:47.381432image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-28T11:58:48.005192image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-28T11:58:48.492586image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-28T11:58:49.115252image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-28T11:58:49.669963image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-28T11:58:50.382213image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-28T11:58:50.897862image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-28T11:58:51.612100image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-28T11:58:47.467125image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-28T11:58:48.070723image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-28T11:58:48.546106image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-28T11:58:49.207687image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-28T11:58:49.767705image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-28T11:58:50.440569image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-28T11:58:50.975431image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-28T11:58:51.672142image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-28T11:58:47.521022image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-28T11:58:48.126256image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-28T11:58:48.603221image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-28T11:58:49.265902image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-28T11:58:49.853936image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-28T11:58:50.499426image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-28T11:58:51.043224image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-28T11:58:51.725991image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-28T11:58:47.596206image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-28T11:58:48.179358image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-28T11:58:48.657132image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-28T11:58:49.319637image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-28T11:58:49.923093image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-28T11:58:50.577240image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-28T11:58:51.109070image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-28T11:58:51.778851image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-28T11:58:47.653917image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-28T11:58:48.243735image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-28T11:58:48.749414image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-28T11:58:49.380550image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-28T11:58:50.017888image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-28T11:58:50.635843image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-28T11:58:51.166943image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-28T11:58:51.865373image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-28T11:58:47.738922image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-28T11:58:48.321173image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-28T11:58:48.862107image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-28T11:58:49.434310image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-28T11:58:50.090798image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-28T11:58:50.692877image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-28T11:58:51.235761image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2025-01-28T11:58:55.905334image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Alcohol_ConsumptionCaffeine_IntakeChest_PainDizzinessDuration_MinutesExercise_FrequencyHeart_RateMedical_History_AnxietyMedical_History_DepressionMedical_History_PTSDMedicationPanic_Attack_FrequencyPanic_ScoreShortness_of_BreathSleep_HoursSmokingSweatingTherapyTremblingTrigger_CaffeineTrigger_PTSDTrigger_PhobiaTrigger_Social AnxietyTrigger_StressTrigger_Unknown
Alcohol_Consumption1.000-0.0100.0210.051-0.0210.0090.0350.0550.0600.0660.000-0.0190.0370.0000.0710.0460.0000.0000.0000.0000.0650.0000.0080.0090.000
Caffeine_Intake-0.0101.0000.0000.000-0.0320.0070.0090.0640.0500.0160.0000.001-0.0090.0290.0170.0000.0450.0000.0000.0580.0590.0230.0000.0000.043
Chest_Pain0.0210.0001.0000.0000.0000.0000.0000.0000.0180.0490.0000.0000.0700.0000.0640.0000.0440.0000.0000.0000.0090.0000.0000.0510.041
Dizziness0.0510.0000.0001.0000.0000.0000.0000.0180.0000.0300.0000.0000.0640.0000.0000.0000.0000.0000.0000.0220.0000.0000.0000.0680.045
Duration_Minutes-0.021-0.0320.0000.0001.000-0.012-0.0010.0370.0440.0000.000-0.0460.0040.071-0.0100.0000.0000.0000.0000.0000.0380.0000.0000.0520.000
Exercise_Frequency0.0090.0070.0000.000-0.0121.000-0.0010.0000.0900.0000.0970.0000.0030.0650.0110.0000.0000.0000.0720.0000.0000.0000.0000.0000.000
Heart_Rate0.0350.0090.0000.000-0.001-0.0011.0000.0000.0280.0000.0590.035-0.0060.048-0.0040.0000.0000.0000.0390.0420.0000.0000.0410.0000.000
Medical_History_Anxiety0.0550.0640.0000.0180.0370.0000.0001.0000.6530.5050.0000.0000.0290.0590.0000.0000.0000.0090.0430.0000.0000.0000.0000.0000.000
Medical_History_Depression0.0600.0500.0180.0000.0440.0900.0280.6531.0000.3140.0000.0420.0390.0560.0000.0000.0000.0000.0430.0000.0000.0000.0210.0000.000
Medical_History_PTSD0.0660.0160.0490.0300.0000.0000.0000.5050.3141.0000.0270.0000.0390.0000.0000.0000.0000.0240.0000.0000.0170.0000.0000.0000.028
Medication0.0000.0000.0000.0000.0000.0970.0590.0000.0000.0271.0000.0660.0490.0000.0000.0000.0000.0030.0320.0000.0000.0410.0000.0720.000
Panic_Attack_Frequency-0.0190.0010.0000.000-0.0460.0000.0350.0000.0420.0000.0661.000-0.0050.068-0.0060.0000.0000.0230.0460.0000.0130.0000.0110.0000.000
Panic_Score0.037-0.0090.0700.0640.0040.003-0.0060.0290.0390.0390.049-0.0051.0000.0000.0170.0470.0650.0000.0610.0000.0000.0660.0000.0000.089
Shortness_of_Breath0.0000.0290.0000.0000.0710.0650.0480.0590.0560.0000.0000.0680.0001.0000.0640.0000.0000.0160.0000.0000.0570.0400.0000.0000.000
Sleep_Hours0.0710.0170.0640.000-0.0100.011-0.0040.0000.0000.0000.000-0.0060.0170.0641.0000.0000.0000.0590.0570.0150.0000.0000.0120.0000.000
Smoking0.0460.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0470.0000.0001.0000.0500.0000.0000.0000.0000.0000.0270.0000.000
Sweating0.0000.0450.0440.0000.0000.0000.0000.0000.0000.0000.0000.0000.0650.0000.0000.0501.0000.0000.0000.0090.0000.0000.0550.0860.000
Therapy0.0000.0000.0000.0000.0000.0000.0000.0090.0000.0240.0030.0230.0000.0160.0590.0000.0001.0000.0080.0000.0000.0000.0140.0000.000
Trembling0.0000.0000.0000.0000.0000.0720.0390.0430.0430.0000.0320.0460.0610.0000.0570.0000.0000.0081.0000.0220.0000.0000.0000.0000.026
Trigger_Caffeine0.0000.0580.0000.0220.0000.0000.0420.0000.0000.0000.0000.0000.0000.0000.0150.0000.0090.0000.0221.0000.1990.1980.1940.1880.200
Trigger_PTSD0.0650.0590.0090.0000.0380.0000.0000.0000.0000.0170.0000.0130.0000.0570.0000.0000.0000.0000.0000.1991.0000.2000.1960.1900.202
Trigger_Phobia0.0000.0230.0000.0000.0000.0000.0000.0000.0000.0000.0410.0000.0660.0400.0000.0000.0000.0000.0000.1980.2001.0000.1950.1890.200
Trigger_Social Anxiety0.0080.0000.0000.0000.0000.0000.0410.0000.0210.0000.0000.0110.0000.0000.0120.0270.0550.0140.0000.1940.1960.1951.0000.1850.197
Trigger_Stress0.0090.0000.0510.0680.0520.0000.0000.0000.0000.0000.0720.0000.0000.0000.0000.0000.0860.0000.0000.1880.1900.1890.1851.0000.190
Trigger_Unknown0.0000.0430.0410.0450.0000.0000.0000.0000.0000.0280.0000.0000.0890.0000.0000.0000.0000.0000.0260.2000.2020.2000.1970.1901.000

Missing values

2025-01-28T11:58:51.956070image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-01-28T11:58:52.154471image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Medical_History_AnxietyMedical_History_DepressionMedical_History_PTSDTrigger_CaffeineTrigger_PTSDTrigger_PhobiaTrigger_Social AnxietyTrigger_StressTrigger_UnknownSweatingShortness_of_BreathDizzinessChest_PainTremblingMedicationSmokingTherapyPanic_Attack_FrequencyDuration_MinutesHeart_RateCaffeine_IntakeExercise_FrequencySleep_HoursAlcohol_ConsumptionPanic_Score
01.00.00.00.00.01.00.00.00.01.00.00.01.00.00.00.01.00.8888890.6410260.7848100.21.0000000.421.0000000.777778
11.00.00.00.00.00.00.01.00.01.01.01.00.00.01.00.01.00.5555560.3589740.6329111.00.0000000.580.4444440.444444
21.00.00.01.00.00.00.00.00.00.01.00.00.00.00.00.00.00.0000000.7435900.8607590.80.3333330.180.2222220.333333
31.00.00.00.00.00.00.00.01.01.01.00.00.01.01.01.01.00.2222220.6410260.9873420.60.3333330.640.8888890.777778
41.00.00.00.00.01.00.00.00.01.01.00.00.00.00.00.00.00.3333330.4102560.2151900.00.1666670.440.4444440.333333
50.01.00.00.01.00.00.00.00.00.01.01.01.01.00.00.01.00.2222220.2820510.6582280.80.3333330.180.2222220.555556
60.01.00.00.00.00.00.00.01.01.01.00.01.01.00.00.00.00.1111110.2820510.1898731.00.6666670.680.2222220.777778
71.00.00.00.00.00.00.00.01.01.00.01.00.01.00.00.00.00.6666670.8717950.4556960.40.6666670.520.3333330.222222
81.00.00.00.01.00.00.00.00.01.00.00.01.00.01.01.00.00.2222220.7435900.9620250.60.6666670.961.0000000.777778
90.01.00.00.00.00.00.00.01.01.01.00.00.00.01.00.01.00.4444440.7948720.2278480.20.1666670.000.1111110.444444
Medical_History_AnxietyMedical_History_DepressionMedical_History_PTSDTrigger_CaffeineTrigger_PTSDTrigger_PhobiaTrigger_Social AnxietyTrigger_StressTrigger_UnknownSweatingShortness_of_BreathDizzinessChest_PainTremblingMedicationSmokingTherapyPanic_Attack_FrequencyDuration_MinutesHeart_RateCaffeine_IntakeExercise_FrequencySleep_HoursAlcohol_ConsumptionPanic_Score
11901.00.00.00.01.00.00.00.00.00.01.00.01.01.01.01.01.00.2222220.6923080.7341770.01.0000000.660.6666671.000000
11911.00.00.00.01.00.00.00.00.00.01.01.00.00.01.00.00.00.2222220.0000000.4683541.00.6666670.541.0000000.666667
11921.00.00.00.00.00.00.00.01.01.00.01.01.01.01.00.00.00.2222221.0000000.0379750.20.0000000.960.6666670.444444
11931.00.00.00.00.00.00.01.00.01.00.01.01.01.00.01.00.00.5555560.1794870.1265820.40.0000000.380.6666670.666667
11941.00.00.00.01.00.00.00.00.00.01.00.00.01.00.00.00.00.7777780.4615380.8354430.00.3333330.380.2222220.111111
11951.00.00.00.00.00.00.01.00.01.01.00.01.01.00.00.01.00.7777780.0256410.1645570.40.0000000.100.4444440.666667
11960.01.00.00.00.00.00.01.00.01.01.00.01.00.01.01.00.00.6666670.8461540.6708860.80.5000000.520.8888890.555556
11970.00.01.00.01.00.00.00.00.00.01.00.01.01.00.00.00.00.8888890.4615380.2911390.00.1666670.800.3333330.666667
11980.00.01.01.00.00.00.00.00.00.01.00.00.00.00.00.01.00.2222220.3846150.2025320.20.3333330.360.1111110.222222
11991.00.00.00.01.00.00.00.00.01.01.00.00.01.01.00.00.00.6666670.1282050.7215190.40.3333330.180.7777780.777778